Research and application of reliability-based cost optimization method for constant interval preventive maintenance

Preventive maintenance is a means to ensure the component is kept in the desired state. Lack of preventive maintenance will cause unexpected consequences for the component, and too much preventive maintenance will result in unnecessary investment of resources. Based on the reliability data of the component, this paper establishes an analysis model to determine the optimal preventive maintenance interval of the component to make the cost of preventive and corrective maintenance lowest.

Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations' expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment's preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment's preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce.

Assessment of the effectiveness of corrective maintenance of an oil pump using the proportional intensity model (PIM)

PurposeComplex repairable systems (CRSs) are generally modeled by stochastic processes called “point processes.” These are generally summed up in the nonhomogeneous Poisson process (NHPP) and the renewal process (RP), which represent the minimum and maximum repair, respectively. However, the industrial environment affects systems in some way. This is why the main objective of this work is to model the CRS with a concept reflecting the real state of the system by incorporating an indicator in the form of covariate. This type of model, known as the proportional intensity model (PIM), will be analyzed with simulated failure data to understand the behavior of the failure process, and then it will be tested for real data from a petroleum company to evaluate the effectiveness of corrective actions carried out.Design/methodology/approachTo solve the partial repair modeling problem, the PIM was used by introducing, on the basis of the NHPP model, a multiplicative scaling factor, which reflects the degree of efficiency after each maintenance action. Several values of this multiplicative factor will be considered to generate data. Then, based on the reliability and maintenance history of 12-year pump's operation obtained from the SONATRACH Company (south industrial center (CIS), Hassi Messaoud, Algeria), the performance of the PIM will be judged and compared with the model of NHPP and RP in order to demonstrate its flexibility in modeling CRS. Using the maximum likelihood approach and relying on the Matlab software, the best fitting model should have the largest likelihood value.FindingsThe use of the PIM allows a better understanding of the physical situation of the system by allowing easy modeling to apply in practice. This is expressed by the value which, in this case, represents an improvement in the behavior of the system provided by a good quality of the corrective maintenance performed. This result is based on the hypothesis that modeling with the PIM can provide more clarification on the behavior of the system. It can indicate the effectiveness of the maintenance crew and guide managers to confirm or revise their maintenance policy.Originality/valueThe work intends to reflect the real situation in which the system operates. The originality of the work is to allow the consideration of covariates influencing the behavior of the system during its lifetime. The authors focused on modeling the degree of repair after each corrective maintenance performed on an oil pump. Since PIM does not require a specific reliability distribution to apply it, it allows a wide range of applications in the various industrial environments. Given the importance of this study, the PIM can be generalized for more covariates and working conditions.

Study of the origins and mechanisms responsible for the occurrence of faults in reinforced concrete structures

Failures in the planning and execution of a work reduce the useful life of the structure, so bring on excessive expenses with corrective maintenance. The life of a building can be divided into four phases, being: design, execution, preventive maintenance and corrective maintenance. Throughout their useful life, systems and construction elements require maintenance actions to be able to maintain their safe conditions. However, pathologies can originate in the conception stage, execution or use of the structure, the causes being linked to characteristic, superficial factors and physical processes. In this context, it was noticed the need to carry out scientific studies on the behavior of structures and the problems they may cause in them, emerging then to the area of pathology in the context of civil engineering. In this research, look for understand the relationship between the materials used, the transport mechanisms of aggressive agents, the deterioration mechanisms and the environment, with the appearance of pathological symptoms in reinforced concrete structures. For this purpose, will be used a bibliographical study and a survey of the types of pathologies in reinforced concrete structures, the identification of causes, the techniques used to correct problems and the materials recommended for use in repairs. Furthermore, the analysis of some case studies will be adopted, with photographic records of the problems found, where the causes of anomalies will be exposed, and measures that were taken in order to solve the pathologies. It is expected with the results to understand the origins and mechanisms responsible for the occurrence of faults and pathologies in reinforced concrete structure.

Static Integration Based on Stochastic P-Time Petri Nets of Maintenance Scheduling for Railway Transport Equipment

In transport systems, all equipment requires maintenance, which directly affects the machine’s availability and consequently the planned transport schedule. The purpose of this paper is to carry out a method for integrating recovery jobs in railway systems. The proposed method allows the insertion of preventive and corrective maintenance operations when the transport equipment is available in order to minimize periods of inactivity, avoid catastrophic scenarios, and maintain stability and safety of the studied networks. A computing algorithm, allowing insertion of the planned recovery tasks in periods of metro availability, without changing the initial scheduling solution, is established. Finally, we illustrate the implementation of the proposed approach on Tunisian Sahel railway transport networks.

Condition-based Maintenance Optimization for Gamma Deteriorating Systems under Performance-based Contracting

With the further development of service-oriented, performance-based contracting (PBC) has been widely adopted in industry and manufacturing. However, maintenance optimization problems under PBC have not received enough attention. To further extend the scope of PBC’s application in the field of maintenance optimization, we investigate the condition-based maintenance (CBM) optimization for gamma deteriorating systems under PBC. Considering the repairable single-component system subject to the gamma degradation process, this paper proposes a CBM optimization model to maximize the profit and improve system performance at a relatively low cost under PBC. In the proposed CBM model, the first inspection interval has been considered in order to reduce the inspection frequency and the cost rate. Then, a particle swarm algorithm (PSO) and related solution procedure are presented to solve the multiple decision variables in our proposed model. In the end, a numerical example is provided so as to demonstrate the superiority of the presented model. By comparing the proposed policy with the conventional ones, the superiority of our proposed policy is proved, which can bring more profits to providers and improve performance. Sensitivity analysis is conducted in order to research the effect of corrective maintenance cost and time required for corrective maintenance on optimization policy. A comparative study is given to illustrate the necessity of distinguishing the first inspection interval or not.

Condition-based Maintenance Optimization for Gamma Deteriorating Systems under Performance-Based Contracting

As a new form of support contract, performance-based contracting (PBC) has been extensively adopted in industry and manufacturing recent years. However, maintenance optimization problems under PBC have not received enough attention. In order to further expand the application of PBC in the field of maintenance optimization, we investigate the condition-based maintenance (CBM) optimization for gamma deteriorating systems under PBC. Considering the repairable single-component system subject to the gamma degradation process, this paper proposes a CBM optimization model with an objective of maximizing the profit and improving system performance at a lower cost under PBC. In the proposed CBM model, first inspection interval is considered to reduce the inspection frequency and the cost. Then, a particle swarm algorithm (PSO) and related solution procedure are presented in order to solve the multiple decision variables in our proposed model. Finally, a numerical example is provided to illustrate the applicability and effectiveness of our proposed model. Through comparing the proposed policy with the conventional one, the superiority of our proposed policy is proved, which can bring more profits to providers and improve performance. Sensitivity analysis is conducted to investigate the effect of corrective maintenance cost and time required for corrective maintenance to optimization policy. Comparative study is given to illustrate the necessity of distinguishing first inspection interval and repeat inspection interval.